The Cryosphere, 9, 557–564, 2015

www.the-cryosphere.net/9/557/2015/

doi:10.5194/tc-9-557-2015

© Author(s) 2015. CC Attribution 3.0 License.

Brief Communication: Contending estimates of 2003–2008

glacier mass balance over the Pamir–Karakoram–Himalaya

A. Kääb1, D. Treichler1, C. Nuth1, and E. Berthier2

1Department of Geosciences, University of Oslo, P.O. Box 1047, Oslo, Norway2CNRS, Université de Toulouse, LEGOS, 14 avenue Ed. Belin, 31400 Toulouse, France

Correspondence to: A. Kääb (kaeaeb@geo.uio.no)

Received: 20 October 2014 – Published in The Cryosphere Discuss.: 25 November 2014

Revised: 12 February 2015 – Accepted: 3 March 2015 – Published: 19 March 2015

Abstract. We present glacier thickness changes over the en-

tire Pamir–Karakoram–Himalaya arc based on ICESat satel-

lite altimetry data for 2003–2008. We highlight the impor-

tance of C-band penetration for studies based on the SRTM

elevation model. This penetration seems to be of poten-

tially larger magnitude and variability than previously as-

sumed. The most negative rate of region-wide glacier el-

evation change (<−1 m yr−1) is observed for the eastern

Nyainqêntanglha Shan. Conversely, glaciers of the western

Kunlun Shan are slightly gaining volume, and Pamir and

Karakoram seem to be on the western edge of this mass-

gain anomaly rather than its centre. For the Ganges, Indus

and Brahmaputra basins, the glacier mass change reaches

−24± 2 Gt yr−1, about 10 % of the current glacier contribu-

tion to sea-level rise. For selected catchments, we estimate

glacier imbalance contributions to river run-off from a few

percent to greater than 10 %.

1 Introduction and methods

Region-wide measurements of glacier volume or mass

change are limited for the Pamir–Karakoram–Himalaya re-

gion, leaving room for speculation about the glacier response

to climate change and its hydrological significance. Glacier

mass change in high mountain Asia (or some part of it) have

been obtained by (i) extrapolating the few existing in situ

mass balance series (Cogley, 2011; Bolch et al., 2012; Yao et

al., 2012), (ii) space gravimetry (Jacob et al., 2012; Gardner

et al., 2013), (iii) laser altimetry (Kääb et al., 2012; Gard-

ner et al., 2013; Neckel et al., 2014) and (iv) the differencing

of digital elevation models (Gardelle et al., 2013). Between

these studies that narrow down the range of uncertainties for

core parts of this remote mountain region, significant incon-

sistencies remain.

The aims of this study are (i) to provide a new consis-

tent regional-scale data set from the ICESat autumn laser

campaigns (2003–2008) by extending Kääb et al. (2012) to

completely cover the study region by Gardelle et al. (2013)

and several major river basins, (ii) to compare the results to

other previous estimates of the Pamir–Karakoram–Himalaya

glacier volume change and (iii) to roughly evaluate the con-

tribution of glacier mass change to river run-off.

We follow the methods explained in Kääb et al. (2012)

with a considerable extension towards the eastern Nyain-

qêntanglha Shan, the Pamir and part of the Tibetan Plateau

(Fig. 1). In short, ICESat footprints are intersected with

the February 2000 SRTM DEM and overlaid on the most

snow-free multispectral Landsat images over ∼ 2000–2013

to manually classify footprints into three classes: glaciers,

non-glaciers and water. Glacier elevation difference trends

are then estimated regionally and at a 1◦× 1◦ geographic

grid by fitting a robust linear temporal trend to the time se-

ries of elevation differences between the SRTM DEM and

individual ICESat footprint elevations. Trends are derived

from autumn ICESat campaigns only (2009 ICESat winter

campaigns excluded), because combined autumn and winter

trends are sensitive to temporal variations in accumulation

amount and timing, potentially introducing bias (see Sup-

plement of Kääb et al., 2012). We confirm that our trends

are not due to sampling bias of ICESat elevations by com-

paring ICESat elevation histograms with glacier hypsometry.

The resulting elevation difference trends for all our zones are

given in Table 1.

Published by Copernicus Publications on behalf of the European Geosciences Union.

558 A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya

Table 1. Glacier elevation difference trends over the Pamir–Karakoram–Himalaya from this and other studies. Note that Gardelle et al. (2013)

cover the period 2000 to ∼ 2010, while the other studies cover 2003 to 2008/2009. Note also that the zones of this study and Gardelle et

al. (2013) coincide whereas the zones of the other do so only roughly, which can potentially explain parts of the disagreements. See text in

Sects. 3 and 4 for an explanation of how the glacier areas were estimated.

Zone Glacier This study Gardner et al. Neckel et al. Gardelle et al.

area (m yr−1, (2013; m yr−1, (2014; m yr−1, (2013; m yr−1,

(km2) ± at 1σ -level) ± at 2σ -level) ± at 1σ -level) ± at 1σ -level)

Eastern Nyainqêntanglhaa 6000 −1.34± 0.29 −0.30± 0.13 −0.81± 0.32 −0.39± 0.16

−0.40± 0.41b

Bhutan 3500 −0.89± 0.16 −0.89± 0.18 −0.78± 0.27 −0.26± 0.15

Everest 8500 −0.37± 0.10 −0.44± 0.20 −0.30± 0.16

West Nepal 7500 −0.43± 0.09 −0.44± 0.26 −0.38± 0.16

Spiti–Lahaul 9500 −0.49± 0.12 −0.53± 0.13 −0.53± 0.16

Karakoram 21 000 −0.10± 0.06 −0.12± 0.15 +0.12± 0.19

Hindu Kush 5500 −0.49± 0.10 −0.14± 0.19

Pamir 6500 −0.48± 0.14 −0.13± 0.22 +0.16± 0.15

Western Kunlun Shan–Tarim 12 500 +0.05± 0.07 +0.17± 0.15 +0.04± 0.29

Area-weighted mean 80 500 −0.37± 0.10

a Named Hengduan Shan in Gardelle et al. (2013); b two zones of Gardner et al. (2013) overlap with our zone and both their values are given.

Figure 1. Study region and trends of elevation differences during 2003–2008. Data are shown on a 1◦ grid with overlapping rectangular

geographic averaging cells of 2◦× 2◦. Trends are based on autumn ICESat acquisitions. Only ICESat footprints over glaciers are indicated.

The zones indicated by black outlines are equivalent to the ones of Gardelle et al. (2013) with the western Kunlun Shan–Tarim zone (dashed

outline) being the only additional one. Trends for all cells (coloured data circles) are statistically significant except for the cells that are

marked with grey centres. The uncertainty of the temporal trends per cell is indicated through circle sizes indirectly proportional to the

standard error of trends at 68 % level.

2 Glacier thickness changes

2.1 Thickening in the Karakoram and western Kunlun

Shan

A first striking feature in the regional map of elevation dif-

ference trends (Fig. 1) is glacier thickness gain in the west-

ern Kunlun Shan (∼+0.1 m yr−1), agreeing with in situ

mass balance and length change measurements (Yao et al.,

2012). There is a southwest-to-northeast gradient from con-

siderably negative glacier mass balances in Hindu Kush and

Spiti–Lahaul to positive values in the Pamir–Karakoram–

western-Kunlun-Shan region (Fig. 1). This suggests the so-

called Karakoram glacier mass-balance anomaly (Hewitt,

The Cryosphere, 9, 557–564, 2015 www.the-cryosphere.net/9/557/2015/

A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya 559

2011; Gardelle et al., 2012a), or Pamir–Karakoram anomaly

(Gardelle et al., 2013), is rather the edge or southwest limit

of an anomaly centred more to the northeast over the western

Kunlun Shan, or Tarim Basin. The anomaly seems thus in-

deed the result of a larger-scale meteorological or climatic

feature, and peculiarities of the Karakoram topography or

glaciers (e.g. surge type, hypsometry, avalanche contribu-

tion; Hewitt, 2011) do not necessarily play a decisive role.

Combined, the results by Gardner et al. (2013), Neckel et

al. (2014) and the glacier elevation change pattern of Fig. 1

suggest the centre of the anomaly could be located over the

Tibetan Plateau.

Direct precipitation measurements in this region are scarce

and thus trends are uncertain. Satellite-retrieved precipitation

and gauge data (Global Precipitation Climatology Project)

suggest an increase of precipitation over the study region

north of Karakoram and east of Pamir (Yao et al., 2012).

Chinese measurements show increased precipitation over the

Tibetan Plateau (C.-Y. Xu, personal communication, 2014),

and Tao et al. (2014) suggest wetter conditions over the Tarim

Basin since the mid 1980s. A number of abnormally wet

years occurred during the early 21st century over the Tarim

Basin and the Tibetan Plateau (Becker et al., 2013), in par-

ticular for the hydrological years 2003/2004 and 2005/2006.

A recent climate modelling study proposes that stable or in-

creasing snowfalls characterise the Karakoram anomaly on

a background of increasing air temperatures (Kapnick et al.,

2014). Despite the available studies and data, further research

seems necessary to consolidate the precipitation and temper-

ature trends and the reason behind the slight glacier volume

gains.

2.2 Massive thinning in eastern Nyainqêntanglha Shan

and Spiti–Lahaul

The other striking feature in Fig. 1 is the massive glacier

thickness loss in the eastern Nyainqêntanglha Shan (between

−1 and −1.7 m yr−1), also consistent with the large negative

mass balances and frontal retreats in this zone (Yao et al.,

2012). The glaciers of eastern Nyainqêntanglha Shan have

the smallest total elevational range in our study region, in-

dicating a large sensitivity to fluctuations in the equilibrium

line altitude (Pelto, 2010; Loibl et al., 2014). The few avail-

able in situ mass balance measurements in the area suggest

that the equilibrium line was over the vertical limits of the

monitored glaciers in the late 2000s, and precipitation in this

zone shows the strongest long-term decrease over the en-

tire Pamir–Karakoram–Himalaya region (Yao et al., 2012;

Becker et al., 2013). A similar pattern of glacier shrinkage,

though less distinct, is found at the western end of the Great

Himalaya Range within our Spiti–Lahaul zone and forms the

cluster of second-largest thickness loss rates in this study

(−0.5 to −0.7 m yr−1). Also here, Landsat data indicate that

firn lines have risen in several years towards high glacier el-

evations, resulting in very small accumulation areas or even

their complete loss.

The 2003–2008 glacier thickness changes in the other

study zones are all similar, on the order of ∼−0.4 to

−0.5 m yr−1 (Table 1), with more negative values in the

Bhutan zone at the transition between the eastern Nyaiqên-

tanglha and Everest zones. We note that glaciers dominated

by the summer monsoon (i.e. east of the Spiti–Lahaul) all

show thickness losses (summer-accumulation type glaciers;

Fujita, 2008; Kapnick et al., 2014; Maussion et al., 2014).

Eastern Nyaiqêntanglha Shan, the zone with strongest glacier

thickness loss, receives most accumulation during March–

May (spring-accumulation type; Maussion et al., 2014). The

glaciers with considerable winter accumulation under influ-

ence of the westerlies show a more mixed picture, with stable

or growing thicknesses in the Karakoram and western Kun-

lun Shan but thickness losses for instance in the Hindu Kush.

2.3 Comparison to previous thickness change studies

The following comparison to other studies uses average

glacier thickness changes rather than total mass changes in

order to minimise effects from different delineations of study

zones, glacier cover areas and density assumptions. From

Hindu Kush and Karakoram in the west to Nepal in the east,

results of all studies agree within their errors (Table 1). Re-

sults are most sensitive to zone delineation in the Hindu

Kush, reflecting the strong spatial variability of glacier thick-

ness change rates in this area (Fig. 1) and presumably also lo-

cally heterogeneous glacier behaviour (Sarikaya et al., 2012;

see also below for Pamir).

Significant differences among the results of all studies are

found over eastern Nyainqêntanglha Shan. Our results and

those from Neckel et al. (2014) agree within the errors but

not with Gardner et al. (2013), although all three studies are

based on ICESat. While our study and Neckel et al. (2014)

use ICESat footprint classifications from contemporary satel-

lite images, Gardner et al. (2013) use Randolph Glacier In-

ventory outlines (RGI version 2.0; Pfeffer et al., 2014), which

contain considerable errors of commission and omission in

this zone (see Table 1 in Gardelle et al., 2013). Repeating

our analysis with footprint classifications based on the Ran-

dolph Glacier Inventory results in less negative elevation dif-

ference trends on glaciers (∼ 20 % less negative) due to in-

clusion of non-glacier footprints. Contrarily, the elevation

difference trends on land (−0.10± 0.06 m yr−1 when using

our own footprint classification) become more negative if

ICESat footprints are classified using RGI due to inclusion

of glacier footprints (−0.16± 0.07 m yr−1). The remaining

discrepancy is presumably due to the fact that the ICESat-

based results of Gardner et al. (2013) are averaged from three

different methods. Their results based on autumn footprints

only (method B, Gardner et al., 2013) suggest a thickness

change rate of −0.86 m yr−1, which is in closer agreement

with our results.

www.the-cryosphere.net/9/557/2015/ The Cryosphere, 9, 557–564, 2015

560 A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya

At a first glance, eastern Nyainqêntanglha Shan results

from Gardelle et al. (2013; zone called Hengduan Shan) and

Gardner et al. (2013) seem to agree, but we believe this

might be a coincidence. Previously, we argued why the Gard-

ner et al. (2013) results might be less negative. Addition-

ally, the results in Gardelle et al. (2013) rely crucially on

an estimate of SRTM C-band penetration. Over any glacier

globally, the SRTM radar waves will typically have pene-

trated into the snow and ice, with potential largest penetration

depths through snow and firn and smallest through ice (Kääb

et al., 2012; Dall et al., 2001; Rignot et al., 2001). As a con-

sequence, SRTM glacier elevations do not, in general, reflect

real mid-February 2000 glacier surface elevations but some

lower horizon, the elevation of which depends on, among

others, the dielectric properties and structure of the pene-

trated glacier volume during the SRTM campaign. For ele-

vation difference studies where one of the elevation data sets

is the SRTM, its penetration depth needs to be estimated for

correction, and biases in this estimate translate directly into

offsets in thickness change. Gardelle et al. (2013) used an

average C-band penetration of 1.7 m for eastern Nyainqên-

tanglha Shan estimated from the difference of SRTM C-band

and X-band DEMs (Gardelle et al., 2012b). Here, we extrap-

olate our ICESat elevation trends over 2003–2008 and their

uncertainty back in time to the SRTM acquisition period in

February 2000. Under the coarse assumption that the 2000–

2003 trends equal to those of 2003–2008, the extrapolation

of February 2000 should result in a zero elevation difference

to ICESat since the SRTM DEM was used as elevation ref-

erence. Offsets in this elevation difference for February 2000

are mainly attributed to SRTM radar penetration into ice and

snow (for method and discussion see Kääb et al., 2012). For

eastern Nyainqêntanglha Shan this analysis indicates an av-

erage penetration of 8–10 m (7–9 m if based on the winter

trends that might alternatively be assumed to reflect February

conditions), much more than the 1.7 m assumed in Gardelle

et al. (2013), while the corresponding off-glacier penetration

is not discernible from zero. Clearly, our penetration depth

lies at the high end but remains within the range of pos-

sible C-band phase-centre penetrations (Kääb et al., 2012;

Dall et al., 2001; Rignot et al., 2001). Sakai et al. (2014)

suggest the highest accumulation rates of the entire study re-

gion occur in eastern Nyainqêntanglha Shan, together with

Hindu Kush. Correction of the Gardelle et al. (2013) results

by our present C-band penetration estimate completely rec-

onciles their results with ours. Note, however, that extrapo-

lation of our 2003–2008 elevation difference trend back to

2000 is based on the risky assumption that the 2000–2003

trend equals the 2003–2008 trend.

For the Bhutan zone, Gardelle et al. (2013) estimated a

C-band penetration for February 2000 of 2.4 m, whereas our

extrapolation of ICESat trends suggests around 6 m, which

again reconciles the results of both studies for this zone.

In the Pamir, our results are more negative than Gard-

ner et al. (2013) and in particular Gardelle et al. (2013).

As above, we suggest that our manual classification of ICE-

Sat footprints versus the Randolph Glacier Inventory con-

tributed to the difference between this study and Gard-

ner et al. (2013) (remark: Gardelle et al. (2013) used their

own inventory). Also, the difference between our study

and Gardner et al. (2013) is reduced when only the re-

sults from their Method B (similar to ours) are consid-

ered. Gardelle et al. (2013) find glacier thickness changes

of +0.16± 0.15 m yr−1 over the Pamir, whereas the present

study suggests −0.48± 0.08 m yr−1. Again, we find larger

SRTM C-band penetration of 5–6 m compared to 1.8 m

(Gardelle et al., 2013). Applying the average C-band pene-

tration from the present study again reconciles the results of

both studies. However, comparison of both studies in Pamir

is complicated by a number of glacier surges (Gardelle et al.,

2013) in connection with particularly sparse ICESat glacier

coverage. Superimposing ICESat tracks over Landsat images

and the elevation change map of Gardelle et al. (2013) re-

veals that they cross areas of either strongly positive or neg-

ative elevation change zones from surge waves. The ICESat

trends thus become biased depending on where they sam-

ple surges, and the total ICESat sample size over Pamir is

not large enough to compensate for these effects. The dif-

ferent observation periods for both studies (2000–2011 ver-

sus 2003–2008) may also have considerable impact due to

surge activities and climate interannual variability (Yi and

Sun, 2013).

3 Glacier mass changes and water resources

We assume an average density of 850 kg m−3 for all 2003–

2008 volume changes to convert the thickness changes to wa-

ter equivalent quantities (Huss (2013); see Kääb et al. (2012),

for different density scenarios). The total glacier area is es-

timated using a simple cross product: we multiply the num-

ber of ICESat glacier footprints in each zone with the ra-

tio between the total zone area and total number of ICESat

footprints. Our method to estimate the total glacier areas is

certainly open to discussion, but we prefer the above pro-

cedure over using areas from the Randolph Glacier Inven-

tory because of the large deviations to our estimates, mainly

for eastern Nyainqêntanglha and Pamir, from obviously out-

dated glacier outlines and voids in the Randolph Inventory

(Nuimura et al., 2014). The uncertainty of water equivalent

quantities includes the standard error of the elevation differ-

ence trend fit, the off-glacier trends, an error due to tempo-

ral offset of the ICESat autumn campaigns from maximum

cumulative ablation conditions, an uncertainty of ±20 % for

the glacier cover areas and an uncertainty of ±60 kg m−3 for

density (Kääb et al., 2012; Huss, 2013). The effects of these

individual sources of uncertainty, all converted to error in

mass change, are combined through the root sum of squares

to arrive at the total uncertainty. Note that water equivalent

results from this study are not identical to Kääb et al. (2012),

The Cryosphere, 9, 557–564, 2015 www.the-cryosphere.net/9/557/2015/

A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya 561

Table 2. Glacier thickness and mass changes over the major river

basins of the study area. The discharge equivalent is a unit conver-

sion from mass change and neglects any losses such as by evapora-

tion or to groundwater.

Major river Glacier Elevation Mass change Discharge

basin area difference trend (Gt yr−1) equivalent

(km2) (m yr−1) DE (m3 s−1)

Tarima 15 000 +0.06± 0.08 +0.7± 1.0 +24± 33

Amu Daryab 11 000 −0.43± 0.08 −4.0± 0.8 −128± 25

Indus 25 000 −0.33± 0.04 −7.0± 0.8 −220± 26

Ganges 11 000 −0.44± 0.07 −4.1± 0.6 −130± 20

Brahmaputra 14 000 −1.06± 0.15 −12.6± 1.9 −400± 60

a The Tarim Basin is endorheic. Only parts of the glacier area (∼ 40 %) within the Tarim Basin are

covered in this study. b Endorheic basin.

even when elevation difference trends agree, due to the sim-

plified density assumption and the different glacier area esti-

mates used.

3.1 Comparison to gravimetric mass loss

For the Pamir, Kunlun Shan and Karakoram (zone 8b of

Jacob et al. (2012); note that the Karakoram is part of

their zone 8b, not 8c as suggested by their zone names)

we estimate a glacier mass change of −6± 2 Gt yr−1

for 2003–2008 that agrees well within the error with

Jacob et al. (2012) results from satellite gravimetry

of −5± 10 Gt yr−1 (January 2003–December 2007) and

−8± 9 Gt yr−1 (January 2004–December 2008). For the Hi-

malaya and eastern Nyainqêntanglha Shan (zone 8c of Ja-

cob et al., 2012) we estimate a 2003–2008 glacier mass

change of−19± 3 Gt yr−1 that compares to−3± 12 Gt yr−1

(January 2003–December 2007) and −2± 10 Gt yr−1 (Jan-

uary 2004–December 2008) from satellite gravimetry. Given

their fundamentally different approaches, it is challenging to

discuss potential sources of disagreement between the two

studies in the Himalaya and eastern Nyainqêntanglha Shan.

Groundwater depletion (Rodell et al., 2009), glacier imbal-

ance run-off into endorheic basins (Zhang et al., 2013) and

errors and biases in the ICESat-derived trends as discussed

above and in Kääb et al. (2012) are all likely explanations.

Note that Gardner et al. (2013) offer a second, more negative

gravimetric estimate for the entire combined high mountain

Asia that is, however, not spatially resolved enough to com-

pare to our results. The uncertainties of our results in this en-

tire paragraph are given at 2σ confidence level to better agree

with the uncertainty level in Jacob et al. (2012), whereas else-

where in this contribution uncertainty is provided at 1σ con-

fidence level.

3.2 River run-off

The glaciers of the Tarim Basin (only 40 % of its total glacier

area is covered here, with notably Tien Shan missing) and the

Amu Darya basin (all glacier areas covered) drain into en-

dorheic basins and thus their mass changes do not contribute

to sea-level changes (Table 2). The glacier mass changes in

the Indus, Ganges and Brahmaputra basins from the present

study contributed together∼ 0.06± 0.01 mm yr−1 to eustatic

sea-level rise, that is ∼ 10 % of the current sea-level contri-

bution of 0.71± 0.08 mm yr−1 from glaciers outside the ice

sheets (Gardner et al., 2013).

The discharge equivalent of these mass changes, that is the

annual average glacier imbalance contribution to river run-

off, is given in Table 2 for the major river basins covered.

Note that computation of our discharge equivalents is a pure

unit conversion from Gt yr−1 to m3 s−1, neglecting any hy-

drological processes and with the sole aim to roughly evalu-

ate the relative importance of glacier mass changes for river

flow in the catchments.

The Tarim Basin glaciers most likely stored water over

2003–2008 (+24± 33 m3 s−1 discharge equivalent, DE).

The glacier imbalance contribution to run-off is largest

for Brahmaputra (−400± 60 m3 s−1 DE), followed by the

Indus (−220 m3 s−1 DE), Ganges and Amu Darya (each

−130 m3 s−1 DE). Comparison of the discharge equivalent

of glacier imbalance to measured river run-off is increasingly

biased the further downstream the gauging stations are sit-

uated from the glaciers due to cumulative natural and man-

made losses. It is also important to note that the available run-

off data from literature and databases refer to various time

periods, in parts considerably older than the ICESat period.

Figure 2 illustrates thus only roughly the hydrological sig-

nificance of the 2003–2008 glacier mass change in selected

gauged catchments. For details on the gauging stations used

and the uncertainty of the contributions see Supplement. As

an example, the 2003–2008 glacier imbalance within the up-

per Indus basin at Besham Qila contributes ∼ 6 % to annual

average river discharge (Fig. 2; Supplement), and we roughly

estimate a very similar number for the Amu Darya (Supple-

ment). For the upper Indus basin, the hydrological balance

is under ongoing discussion (Reggiani and Rientjes, 2014),

and we hope that our glacier mass change estimates can con-

tribute towards balance closure and better understanding of

spatial–temporal patterns of run-off or high-elevation precip-

itation amounts in the region (e.g. Immerzeel et al., 2012).

The modelling results for “non-renewable glacier run-

off” of Savoskul and Smakhtin (2013) agree well

with ours for Amu Darya, less for Indus (they ob-

tain −0.55 m w.e. yr−1 specific mass loss rate over 2001–

2010; we have −0.28 m w.e. yr−1) and Ganges (they obtain

−0.77 m w.e. yr−1; we have −0.37 m w.e. yr−1) and not very

well for Brahmaputra (they obtain −0.36 m w.e. yr−1; we

have −0.90 m w.e. yr−1).

www.the-cryosphere.net/9/557/2015/ The Cryosphere, 9, 557–564, 2015

562 A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya

Figure 2. The percentage of discharge equivalent from annual glacier imbalance to measured average river run-off for selected catchments.

Note that the actual numbers will be somewhat lower due to unaccounted water losses such as from evaporation or to groundwater. For details

on the gauging stations used and the uncertainty of the contributions see the Supplement.

4 Conclusions

From 2003 to 2008 ICESat-derived elevation difference

trends over Pamir–Karakoram–Himalaya and from compar-

isons to geographically overlapping studies, we draw the fol-

lowing conclusions:

– Glacier thickness loss over the study region is most

pronounced for the eastern Nyainqêntanglha Shan, fol-

lowed by the western end of the Great Himalaya Range.

Glaciers in and around the western Kunlun Shan are in

balance or even gaining volume, and Pamir and Karako-

ram seem to be on the western limit of this mass balance

anomaly rather than its centre. This suggests it is a me-

teorological or climatic anomaly (rise in precipitation).

However, the cause and duration of this regional glacier

anomaly is not fully understood yet.

– Our glacier volume changes seem especially uncertain

in Pamir and, to a lesser extent, Hindu Kush. The het-

erogeneous behaviour of individual glaciers in these

two zones, for instance from glacier surges, may lead

to biases when extrapolating elevation difference trends

from particularly sparse ICESat tracks, or areas covered

by differential DEMs, to the entire zones.

– Extrapolation of ICESat trends back in time to the

SRTM acquisition date suggests a much larger poten-

tial magnitude and variability of SRTM C-band phase-

centre penetration than often assumed. Given the cru-

cial importance of radar penetration for glacier thick-

ness change studies based on radar DEMs, such as the

SRTM or the upcoming TanDEM-X, we recommend

being critical of penetration assumptions used in previ-

ous studies and to investigate the issue more extensively

and systematically (Langley et al., 2007; chapter 7 in

Müller, 2011). The problem is complicated by the fact

that radar penetration has to be known specifically for

certain dates from the past.

– The glacier mass changes in the Tarim and Amu Darya

basins of +0.7± 1.0 and −4.0± 0.8 Gt yr−1 do not

contribute to sea-level rise. The combined Ganges, In-

dus and Brahmaputra basin glacier mass change is

−23.7± 2.1 Gt yr−1, almost 10 % of the glacier contri-

bution to sea-level rise.

– Neglecting water losses downstream of the glaciers,

the 2003–2008 glacier imbalances amount to ∼ 6 % of

the annual discharge of Amu Darya and upper Indus

where they leave the mountains. This is a considerable

amount given the significance of the rivers for the Aral

Sea (Amu Darya) and massive irrigation schemes and

household use in these dry climate regions. Maximum

glacier imbalance contributions to annual average river

run-off of up to ∼ 17 % are found for the Shyok (Indus)

and ∼ 10 % for Vakhsh (Amu Darya), while minimum

contributions are only ∼ 1–3 % for the monsoon-type

catchments in Nepal.

– Our results on glacier mass loss agree with those from

satellite gravimetry (Jacob et al., 2012) over Pamir,

western Kunlun Shan and Karakoram but significantly

diverge over the Himalaya and eastern Nyainqêntanglha

Shan.

It is important to note that our results only cover 5 years,

2003–2008, and it remains open to what extent those years

are representative for longer periods, such as the 10 years

covered by Gardelle et al. (2013). For short mass balance

series, single anomalous years may have large impacts on

The Cryosphere, 9, 557–564, 2015 www.the-cryosphere.net/9/557/2015/

A. Kääb et al.: Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya 563

trends. Our water equivalent results are also sensitive to den-

sity and glacier area assumptions. We find that glacier out-

lines and areas in the study region are still quite uncertain

and invite the reader to use improved glacier area estimates

for upscaling our results and own assumptions for the con-

version of volume changes to mass changes.

The Supplement related to this article is available online

at doi:10.5194/tc-9-557-2015-supplement.

Author contributions. A. Kääb designed the study, performed the

data analysis and wrote the paper. D. Treichler, C. Nuth and

E. Berthier contributed to data analysis, performed supporting anal-

yses and edited the paper.

Acknowledgements. Sincere thanks are due to Duncan Quincey,

another anonymous referee, colleagues that commented on the

study, and the editor Andreas Vieli for their valuable feedback

that certainly improved our work. The study was funded by the

European Research Council under the European Union’s Seventh

Framework Programme (FP/2007-2013)/ERC grant agreement

no. 320816, the ESA project Glaciers_cci (4000109873/14/I-NB)

and the Department of Geosciences, University of Oslo. E. Berthier

acknowledges support from TOSCA (CNES). We are very grateful

to NASA and NSIDC for free provision for the ICESat data and the

USGS for the SRTM DEM and Landsat imagery.

Edited by: A. Vieli

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